Healthcare-Associated Infections – Bucaramanga, Colombia 2016-2023

A line list of healthcare-associated infections (IAAS, Infecciones Asociadas a la Atencion en Salud) notified in the municipality of Bucaramanga, Santander, Colombia, between January 2016 and January 2023. Each row is one notified infection: a specimen taken from a hospitalised patient, the laboratory result, and the isolated microorganism.

Usage

hai_bucaramanga

Format

A tibble with 1,423 rows and 13 variables:

id: integer. The source's Orden autonumber. Documented as unique but repeated for 100 records (see above), so it is not a reliable key.
specimen_date: Date. The event date – when the sample was taken from the patient. NA for 318 records.
received_date: Date. When the laboratory received the sample. NA for 1,194 records and absent after 2019-12-01.
report_date: Date. The report date – when the laboratory result was issued. NA for 583 records.
specimen: factor, 6 levels. Sample type: "Whole blood", "Urine", "Secretions", "Other sterile fluids", "Sputum", "Bronchoalveolar lavage". NA for clinically-confirmed cases.
test: factor, 7 levels. Laboratory test performed, e.g. "Blood culture", "Urine culture". NA for clinically-confirmed cases.
microorganism: character, 85 distinct values. The isolated organism, in conventional binomial form, e.g. "Klebsiella pneumoniae". NA for clinically-confirmed cases. Where the source recorded only a genus the value is "<Genus> spp.".
sex: factor. "Female" or "Male".
age_group: ordered factor, 11 levels from "<1" to "70+".
case_type: factor. "Laboratory-confirmed" (1,090) or "Clinically-confirmed" (333).
final_condition: factor. Patient status on discharge: "Alive" or "Dead". NA for 8 records.
icu_type: factor. Intensive-care unit where the case occurred: "Adult", "Paediatric" or "Neonatal".
institution: integer, 1-10. Anonymised code of the reporting health institution.

Source

Secretaria de Salud y Ambiente de Bucaramanga, via the Colombian national open-data portal https://www.datos.gov.co/Salud-y-Protecci-n-Social/48-Infecciones-asociadas-a-la-atenci-n-en-salud-IA/w4zx-wbff/about_data. Retrieved 2026-08-17. Column names and categorical values translated from Spanish; see data-raw/hai_bucaramanga.R for the mapping.

Details

In the nowcasting context the event date is specimen_date (when the sample was taken from the patient) and the report date is report_date (when the laboratory result was issued). The delay between the two is the specimen-to-result turnaround that a nowcasting model would estimate and correct for. A third date, received_date, records when the laboratory received the sample and splits the delay into a transport and a processing leg – but see the warnings below before relying on it.

The column names and all categorical values have been translated from the original Spanish. The dataset has been trimmed to the columns relevant to a delay or nowcasting analysis; see data-raw/hai_bucaramanga.R in the package sources for the full translation tables and the list of dropped columns.

Data quality – read this first

This is a real, unpolished open-data extract, and it is included partly because it is messy: it is a realistic exercise for the delay diagnostics in this package (diagnose_drift(), diagnose_changepoint(), plot_delay_profiles()). Nothing below has been silently repaired.

Missing dates: The source uses 1900-01-01 as an undocumented missing-date sentinel. It has been converted to NA here – left in, it produces delays of about -45,000 days. After conversion specimen_date is present for 1,105 records (77.7%), report_date for 840 (59.0%) and received_date for only 229 (16.1%).
received_date is largely unusable: Beyond being 84% missing, it stops entirely at 2019-12-01, so the three-date chain exists only for the first third of the study period. Prefer the specimen_date -> report_date pair.
Negative delays: 88 records (10.7% of the otherwise-valid pairs) have a report_date before their specimen_date, by up to 331 days. These are kept exactly as recorded. Filter them out before fitting anything.
Exact duplicates: 100 records are byte-identical duplicates of another record, sharing the id that the source documents as a unique autonumber. All 1,423 rows are shipped for fidelity; use dplyr::distinct() to reduce to the 1,323 unique records.
Clinically-confirmed cases: For the 333 cases confirmed on clinical grounds rather than by laboratory, the source wrote the literal string "CONFIRMADO POR CLINICA" into the muestra, nombre prueba and microorganismo fields. Those are NA here; the information is preserved in case_type.
Sparsity: Only 738 records (51.9%) support a non-negative specimen_date -> report_date delay, spread over 488 distinct event dates – roughly 1.5 cases per event date. That is thin for fitting a nowcasting model, though ample for delay diagnostics. Aggregating to weeks or months is usually necessary.

The delay distribution is strongly bimodal: the median is 3 days but the 90th percentile is 92 days, which makes this a useful test case for delay diagnostics that assume a unimodal delay.

Examples

data(hai_bucaramanga)

# The source ships 100 exact duplicates and a number of unusable rows.
# Reduce to unique records with a valid, non-negative reporting delay.
iaas_clean <- dplyr::distinct(hai_bucaramanga) |>
  dplyr::filter(
    !is.na(specimen_date), !is.na(report_date),
    report_date >= specimen_date
  )
nrow(iaas_clean)
#> [1] 673

# Roughly 1.5 cases per event date, so aggregate to weeks before building a
# tbl_now for anything model-shaped.
iaas_now <- tbl_now(
  iaas_clean,
  event_date  = "specimen_date",
  report_date = "report_date",
  verbose     = FALSE
)
iaas_now
#> # A tibble:  673 × 16
#> # Data type: "linelist"
#> # Frequency: Event: `days` | Report: `days`
#>       id specimen_date received_date report_date   specimen  test  microorganism
#>    <int> <date>        <date>        <date>        <fct>     <fct> <chr>        
#>    [...] [event_date]  [...]         [report_date] [...]     [...] [...]        
#>  1     3 2018-01-27    2018-01-27    2018-01-31    Whole bl… Bloo… Stenotrophom…
#>  2     4 2018-01-27    2018-01-27    2018-01-30    Whole bl… Bloo… Klebsiella p…
#>  3     5 2018-04-20    2018-04-20    2018-04-26    Urine     Urin… Klebsiella p…
#>  4     6 2018-01-22    2018-01-22    2018-01-25    Whole bl… Bloo… Klebsiella p…
#>  5     7 2018-01-02    2018-01-02    2018-03-02    Whole bl… Bloo… Klebsiella p…
#>  6     8 2018-06-28    2018-06-28    2018-06-30    Whole bl… Bloo… Acinetobacte…
#>  7    13 2018-10-03    2018-10-03    2018-12-03    Urine     Urin… Enterobacter…
#>  8    14 2018-05-03    2018-05-03    2018-08-03    Urine     Urin… Candida albi…
#>  9    15 2018-07-03    2018-07-03    2018-08-03    Whole bl… Bloo… Acinetobacte…
#> 10    16 2018-03-17    2018-03-17    2018-03-20    Urine     Urin… Staphylococc…
#> # ────────────────────────────────────────────────────────────────────────────────
#> # Now: 2023-02-01 | Event date: "specimen_date" | Report date: "report_date"
#> # ────────────────────────────────────────────────────────────────────────────────
#> # ℹ 663 more rows
#> # ℹ 9 more variables: sex <fct>, age_group <ord>, case_type <fct>,
#> #   final_condition <fct>, icu_type <fct>, institution <int>, .event_num <dbl>,
#> #   .report_num <dbl>, .delay <dbl>

# The delay is strongly bimodal: a 3-day median with a long secondary mode.
quantile(iaas_now$.delay, c(0.5, 0.75, 0.9, 0.99), na.rm = TRUE)
#> 50% 75% 90% 99% 
#>   3  31  92 153